Cellular communication systems are designed for peak hour traffic despite the fact that the traffic activity in a certain area is high only during short periods of the day. Considering the traffic activity in a residential area, it is usually low during daytime when people have left homes for work while it increases in the evenings when people are at home. The opposite pattern prevails for the office area. In a heterogeneous network environment different infrastructures are differently utilized throughout the duration of a day, e.g. macro cells are serving traffic in a residential area during working hours while a vast amount of the traffic is carried by pico or femto cells during evenings and late hours. At the opposite end of the city in the working areas these indoor femto cells or outdoor pico cells that provide indoor coverage are not fully utilized at the end of business days or during evenings and nights. In all cases under-utilization of the radio infrastructure and radio resources is a waste of power and significant power savings could be achieved by reducing the number of radio resources that the network provides in time, space and frequency, e.g. by switching off a number of base stations at different tiers (micro, pico, femto, etc.).
One of the simplest approaches to obtain energy efficiency is based on the activation of network resources on demand, thus avoiding to always power on all the resources that are necessary to serve the mobile users during peak traffic periods. This necessitates the implementation of a power on/off strategy that refers to the switching of radio infrastructure nodes and cells of a radio network. The radio network could be a heterogeneous network (HetNet) consisting of sites with different power transmission, coverage and capacity profiles. One of the key optimization problems in such HetNet scenario is to maximize or maintain user throughput and coverage at a minimum of energy consumption cost.
One prior art solution proposes an iterative algorithm based on a simulated-annealing search. The algorithm is centralized meaning that it executes in a single point using information about all base stations and all mobile users in the network. The centralized approach has the following drawbacks:
does not scale well to large networks with thousands of base station nodes and ten thousands of mobile users since (a) optimization complexity may be too great, (b) there will be more local maxima so there is a higher risk that the optimization algorithm chooses one of these, and (c) the gain matrix becomes very large and it is not stated how the gain matrix can be determined;
network availability is at risk because the central computation entity represents a single point of failure.
There are other prior art solutions on energy savings in wireless networks. Some consider switching off a group of cells, whilst others focus on specific radio access technologies characteristics, e.g. UMTS and/or use different optimization approaches. These solutions do not consider the trade-off between throughput and power consumption, and secondly do not consider the details of switching on/off individual cells.
Another drawback of the methods described above is that they are not feasible in practice. By switching group of cells according to a pattern it is not possible to flexibly address mobile users' needs in locations where non-uniform user distribution and non-uniform service demands occur. Moreover, the proposed optimization algorithms are often computationally intractable and do not scale for large cellular networks.